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Fulltext COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia

Lai H, Khan YA, Thaljaoui A, Chammam W, Abbas SZ

Soft comput, 2021 May 19.

PMID: 34025212 DOI: 10.1007/s00500-021-05871-6

Unemployment remains a serious issue for both developed and developing countries and a driving force to lose their monetary and financial impact. The estimation of the unemployment rate has drawn researchers' attention in recent years. This investigation's key objective is to inquire about the impact of COVID-19 on the unemployment rate in selected, developed and developing countries of Asia. For experts and policymakers, effective prediction of the unemployment rate is an influential test that assumes an important role in planning the monetary and financial development of a country. Numerous researchers have recently utilized conventional analysis tools for unemployment rate prediction. Notably, unemployment data sets are nonstationary. Therefore, modeling these time series by conventional methods can produce an arbitrary mistake. To overcome the accuracy problem associated with conventional approaches, this investigation assumes intelligent-based prediction approaches to deal with the unemployment data and to predict the unemployment rate for the upcoming years more precisely. These intelligent-based unemployment rate strategies will force their implications by repeating diversity in the unemployment rate. For illustration purposes, unemployment data sets of five advanced and five developing countries of Asia, essentially Japan, South Korea, Malaysia, Singapore, Hong Kong, and five agricultural countries (i.e., Pakistan, China, India, Bangladesh and Indonesia) are selected. The hybrid ARIMA-ARNN model performed well among all hybrid models for advanced countries of Asia, while the hybrid ARIMA-ANN outperformed for developing countries aside from China, and hybrid ARIMA-SVM performed well for China. Furthermore, for future unemployment rate prediction, these selected models are utilized. The result displays that in developing countries of Asia, the unemployment rate will be three times higher as compared to advanced countries in the coming years, and it will take double the time to address the impacts of Coronavirus in developing countries than in developed countries of Asia.

Fulltext RETRACTED ARTICLE: Internet of vehicles and autonomous systems with AI for medical things

Ghazal TM, Said RA, Taleb N

Soft comput, 2023;27(2):1211.

PMID: 34316287 DOI: 10.1007/s00500-021-06035-2

Fulltext Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India

Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, et al.

Soft comput, 2023 May 29.

PMID: 37362259 DOI: 10.1007/s00500-023-08596-w

[This retracts the article DOI: 10.1007/s00500-021-06012-9.].

Fulltext Weather indicators and improving air quality in association with COVID-19 pandemic in India

Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, et al.

Soft comput, 2023;27(6):3367-3388.

PMID: 34276248 DOI: 10.1007/s00500-021-06012-9

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

Fulltext COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, et al.

Soft comput, 2023;27(5):2657-2672.

PMID: 33250662 DOI: 10.1007/s00500-020-05424-3

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

Retraction Note: COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, et al.

Soft comput, 2024;28(Suppl 1):65.

PMID: 39847664 DOI: 10.1007/s00500-024-09992-6

[This retracts the article DOI: 10.1007/s00500-020-05424-3.].

Fulltext RETRACTED ARTICLE: Social media analytics and their applications to evaluate an activity in online health interventions using CRITIC and TOPSIS techniques

Lin Y, Alshehri Y, Alnazzawi N, Abid M, Khan SA, Jabeen F, et al.

Soft comput, 2024 Dec;28(Suppl 2):481.

PMID: 37362268 DOI: 10.1007/s00500-023-08004-3

Fulltext RETRACTED ARTICLE: VIKOR and TOPSIS framework with a truthful-distance measure for the (t, s)-regulated interval-valued neutrosophic soft set

Quek SG, Garg H, Selvachandran G, Palanikumar M, Arulmozhi K, Smarandache F

Soft comput, 2024 Dec;28(Suppl 2):553.

PMID: 37362303 DOI: 10.1007/s00500-023-08338-y

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